There are four tightly-integrated key components to our proposed approach to enable 3D through-wall imaging, as shown in the figure below. First, we have proposed robotic paths that can capture the spatial variations in all the three dimensions as much as possible while maintaining the efficiency of the operation. Second, we have modeled the 3D unknown area of interest as a Markov Random Field and utilized Loopy Belief Propagation to update the imaging decision of each voxel. In order to approximate the interaction of the transmitted wave with the area of interest, we have used the WKB linear wave model. Finally, we have also taken advantage of the compressibility of the information content to image the area with a very small number of WiFi measurements (less than 4%), using sparse signal processing.
The field of data analytics is growing as fast as the internet itself. Self-driving cars, airline pricing, and huge marketing campaigns are all driven by the insights that data scientists can distill out of vast sums of information. Even with the help of powerful software like Python, it’s a highly skilled position. But those skills […]
If you’re marketing on the web, your Google-fu needs to be strong – and up to date. Without a firm grasp on what drives traffic, you’ll never be able to take the wheel. That’s why even if you know where to put your keywords, a little extra effort goes a long way on any marketer’s […]
Want to keep the dentist away? A little tooth care at morning and night isn’t bad, but it won’t keep the stains from smoking or fried foods at bay for long. If you enjoy your food and want to avoid the consequences, an upgrade from that old analog toothbrush can make a huge difference. Among […]